Classificação de Gêneros Musicais Latinos e suas Emoções: Abordagens Bayesiana e Fuzzy

Glaucia Maria Bressan, Beatriz Cristina Flamia de Azevedo


Este trabalho tem como objetivo classificar automaticamente gêneros musicais latinos considerando suas emoções predominantes. Os métodos propostos são baseados no método de classificação  fuzzy e no método de classificação Bayesiano, o qual utiliza o algoritmo BayesRule. Estas duas metodologias extraem regras de classificação linguísticas, o que possibilita que seja feita uma comparação entre os resultados obtidos, além da classificação inteligente do conjunto de dados considerando incertezas e fusões entre os gêneros musicais.


gêneros musicais; classificação fuzzy; classificação Bayesiana.


G. E. P. Box and G. C. Tiao. Bayesian inference in statistical analysis, 1992.

J. Cheng, R. Greiner, J. Kelly, D. Bell, and W. Liu. Learning bayesian networks from data: An information-theory based approach. Artificial Intelligence, (137):43–90, 2002.

P. J. Donnelly and J. W. Sheppard. Classification of musical timbre using bayesian networks. Computer Music Journal, (4):70–86, 2014.

F. Fernández, F. Chávez, R. Alcalá, and F. Herrera. Musical genre classification by means of fuzzy rule-based systems: A preliminary approach. IEEE Congress on Evolutionary Computation, IEEE CEC, (2):303–319, 2011.

S. Goyal and E. Kim. Application of fuzzy relational interval computing for emotional classification of music. Norbert Wiener in the 21st Century (21CW), 2014 IEEE Conference on, pages 1–8, 2014.

J. Han, M. Kamber, and J. Pei. Data mining: Concepts and techniques, 2011.

E. R. Hruschka Jr, M. C. Nicoletti, V. A. Oliveira, and G. M. Bressan. Markovblanket based strategy for translating a bayesian classifier into a reduced set of classification rules. 7th International Conference on Hybrid Intelligent Systems, pages 192–197, 2007.

K. Kashino, K. Nakadai, T. Konoshita, and H. Tanaka. Application of bayesian probability network to music scene analysis. Computational Auditory Scene Analysis, pages 115–137, 1996.

Y. Kim, E. M. Schmidt, R. Migneco, B. G. Morton, P. Richardson, J. Scott, J. A. Speck, and D. Turnbull. Music emotion recognition: A state of the art review. ISMIR, pages 255–266, 2010.

B. Lerner and R. Malka. Investigation of the k2 algorithm in learning bayesian network classifiers. Applied Artificial Intelligence, (1):74–96, 2011.

N. C. Maddage, C. Xu, and Y. Wang. An svm-based classification approach to musical audio. Proceedings of the 4th International Conference on Music Information Retrieval, 2003.

R. E. Neapolitan. Learning bayesian networks, 2003.

J. Pearl. Probabilistic reasoning in intelligent systems: Networks of plausible inference, 1988.

W. Pedrycz and F. Gomide. An introduction to fuzzy sets, 1998.

C. L. Santos and C.N. Silla Jr. The latin music mood database. Journal on Audio, Speech and Music Processing, 2015.

C. N. Silla Jr, A. L. Koerich, and C. A. A. Kaestner. A feature selection

approach for automatic music genre classification. International Journal of

Semantic Computing, 3(2):183–208, 2009.

C.N. Silla Jr, A.L. Koerich, and C.A.A. Kaestner. The latin music database. Proceedings of 9th International Conference on Music Information Retrieval, pages 451–456, 2008.

D. Temperley. Music and probability, 2007.

R. E. Thayer. The biopsychology of mood and arousal, 1989.

G. Tzanetakis and P. Cook. Marsyas: A framework for audio analysis. Journal Organized Sound, pages 169–175, 1999.

Y. Yang and H. H. Chen. Music emotion recognition, 2011.

Y. Yang, C. Liu, and H. H. Chen. Music emotion classification: A fuzzy approach. Proceedings of the 14th ACM international conference on Multimedia, pages 81–84, 2006.


Article Metrics

Metrics Loading ...

Metrics powered by PLOS ALM


  • There are currently no refbacks.

Trends in Computational and Applied Mathematics

A publication of the Brazilian Society of Applied and Computational Mathematics (SBMAC)


Indexed in:




Desenvolvido por:

Logomarca da Lepidus Tecnologia